In this article, a combination of the wavelet neural network framework and the line-up competition algorithm is used to solve the economic optimization algorithm for an industrial-scale atmospheric distillation column (ADC) process. Compared to the relevant measuring data from Sinopec Wuhan Petroleum Group Company, China, the first optimal operating conditions show that the increments of the duties of furnace and pump-arounds of the ADC can effectively improve oil production. In our approach, the preflash column (PFC) coupled with ADC is denoted as an industrial-scale crude distillation unit (CDU) process. Since the PFC can produce light naphtha and reduce the furnace duty and steam consumption of ADC, it is verified that the CDU process provides the higher economic potential than ADC. Based on the second optimal operating conditions, the plantwide control strategy is employed to operate the system safely as well as regulate the outputs of the plant in the presence of inlet perturbations. Within the plantwide control framework, the inventory control aims to keep the controlled variables close to the desired operating condition and the quality control loops use a combination of inferential predictions and feedforward ratios to effectively suppress the temperature spikes of trays and furnaces. Finally, the simulations show that the product quality is guaranteed due to no offset ASTM D86 distillation temperature responses.